## Abstract

Logistic and probit models are the most popular regression model for binary

outcomes. A simple robust alternative is the robit model, which replaces the

underlying Normal distribution in the probit model with a Student t–distribution.

The heavier tails of the t–distribution (compared with the Normal distribution)

means that model outliers are less influential. Robit regression can be fit as a

generalized linear model with the link function defined as the inverse cumulative t–

distribution function with a specified number of degrees of freedom (df), and it has

been advocated as being particularly suitable for estimating inverse–probability

weights and propensity scoring more generally. Here we describe a new package

called robit that implements robit regression in Stata.

outcomes. A simple robust alternative is the robit model, which replaces the

underlying Normal distribution in the probit model with a Student t–distribution.

The heavier tails of the t–distribution (compared with the Normal distribution)

means that model outliers are less influential. Robit regression can be fit as a

generalized linear model with the link function defined as the inverse cumulative t–

distribution function with a specified number of degrees of freedom (df), and it has

been advocated as being particularly suitable for estimating inverse–probability

weights and propensity scoring more generally. Here we describe a new package

called robit that implements robit regression in Stata.

Original language | English |
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Journal | STATA JOURNAL |

Publication status | Accepted/In press - 1 Nov 2022 |